Geospatial Stuff
University of Auckland
Bioeconomy Science Institute
Collected using QStarz Q1100P GPS Tracking Recorders between 2016 and 2018
40+ hour battery life and simple use
Location recorded every 30 seconds
Scientists turned devices on when leaving base camp in the morning and off on return at the end of the day
Bing maps aerial imagery
Yup! There’s streetview imagery
Start with a regular lattice across the study area
Many topologies could be used
We went with hexagonal at ~100m spacing
Slope is critical to movement, so attach heights to nodes and calculate slope in each direction along lattice edges
Then apply a hiking function to estimate traversal times
Assign edges estimated traversal times based on slope, the hiking function, and land cover (moraine or rock)
Make the nodes and edges into a directed graph
Then use various graph algorithms to find, e.g., everywhere-to-everywhere shortest paths
After some exploration, we settled on betweenness centrality as a graph metric that might be useful
This measure counts how many times each node appears on the shortest paths between every other pair of nodes
An indicator of the relative likelihood of each location being visited
Restrict betweenness centrality to nodes no more than some time apart
This is much faster to calculate (yay!)
Also… it looks like it has more value
It (perhaps) is relevant to how people navigate in such environments
One way to minimise impact might be to plan paths
This is experimental at this stage
Based on a minimum spanning tree approximation to an arborescence
Terrain-differentiated data-fitted hiking functions are a novelty
Potential wider application of radius-limited betweenness centrality?
A manuscript almost finished
On this project: dosull.github.io/antarctica
About Geospatial Stuff: dosull.github.io
Questions?
Palmerston North Regional GIS Forum, September 2025